Identificacion Principal del registro INGC-EBK-000032

Control Number Identifier AR-LpUFI

LAST MODIFICATION DATE 20160826093003

CONTROL FIELD cr nn 008mamaa

CONTROL FIELD 131206s2014 xxk| s |||| 0|eng d

a International Standard Book Number 9781447155713

a Standard number or code 10.1007/978-1-4471-5571-3

a Personal name Du, Ke-Lin.

a Title Neural Networks and Statistical Learning

h Medium [libro electrónico] /

c Statement of responsibility, etc by Ke-Lin Du, M. N. S. Swamy.

a Place of publication, distribution, etc London :

b Name of publisher, distributor, etc Springer London :

b Name of publisher, distributor, etc Imprint: Springer,

c Date of publication, distribution, etc 2014.

a Extent xxvii, 824 p. :

b Other physical details il.

a Formatted contents note Introduction -- Fundamentals of Machine Learning -- Perceptrons -- Multilayer perceptrons: architecture and error backpropagation -- Multilayer perceptrons: other learing techniques -- Hopfield networks, simulated annealing and chaotic neural networks -- Associative memory networks -- Clustering I: Basic clustering models and algorithms -- Clustering II: topics in clustering -- Radial basis function networks -- Recurrent neural networks -- Principal component analysis -- Nonnegative matrix factorization and compressed sensing -- Independent component analysis -- Discriminant analysis -- Support vector machines -- Other kernel methods -- Reinforcement learning -- Probabilistic and Bayesian networks -- Combining multiple learners: data fusion and emsemble learning -- Introduction of fuzzy sets and logic -- Neurofuzzy systems -- Neural circuits -- Pattern recognition for biometrics and bioinformatics -- Data mining.

a Summary, etc Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.

a Topical term or geographic name as entry element Engineering.

a Topical term or geographic name as entry element Data mining.

a Topical term or geographic name as entry element Pattern recognition.

a Topical term or geographic name as entry element Neural networks (Computer science).

a Topical term or geographic name as entry element Computational intelligence.

a Topical term or geographic name as entry element Mathematical Models of Cognitive Processes

a Topical term or geographic name as entry element Knowledge Discovery.

a Personal name Swamy, M. N. S.

u Uniform Resource Identifier (R) http://dx.doi.org/10.1007/978-1-4471-5571-3

c item type EBK

a descripción COM